Department of Remote Sensing, Institute of Geography, Julius-Maximilians-University Würzburg, Würzburg, Germany.
Environ Monit Assess. 2011 May;176(1-4):531-47. doi: 10.1007/s10661-010-1602-5. Epub 2010 Jul 16.
Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.
综合生态系统评估倡议是迈向全球生物多样性观测系统的重要步骤。可靠的地球观测数据是跟踪各种尺度生物多样性变化的关键信息。关于建立标准化环境观测系统，一个关键问题是：在每个尺度上可以观察到什么，以及如何转移土地覆盖信息？在本研究中，基于联合国 LCCS、实地数据以及 MODIS 和 Landsat 卫星图像，获得了纳米比亚干旱半干旱稀树草原生态系统的土地覆盖图。实地植物学样本被用作定义标准化 LCCS 图例的基准数据。引入了用于稀树草原植被类型的标准 LCCS 代码。使用面向对象的 Landsat 图像分割作为中间阶段，将现场训练数据下采样到粗糙的 MODIS 分辨率。使用基于树的集成分类器（随机森林），使用 2004/2005 生长季节的 MODIS 时间序列指标对喀拉哈里植被类型进行分类。基于 LCCS 的主要喀拉哈里植被类型是具有草本层的开阔阔叶落叶灌丛，这与全球和区域土地覆盖图的类别分配不同。基于 Bhattacharya 距离测量的可分离性分析应用于两个 LCCS 级别，表明由于分类方案的主题细节，年度 MODIS 时间序列特征的光谱映射依赖性存在关系。LCCS 分类器的分析表明，生活型组成和土壤条件对映射精度的重要性增加。总体精度达到 92.48%。由于小的遗漏和错误，木本植物组合被证明是最稳定的。该案例研究首次对南部非洲稀树草原生态系统的 LCCS 分类器方法进行了适用性评估。
